Method for evaluating and screening food brewing raw materials and application

Abstract

This invention relates to methods and applications for evaluating and screening food brewing raw materials, belonging to the field of fermented food preparation technology. This invention utilizes response surface methodology to construct predictive models. By analyzing the protein or glycosylation profiles of cereal raw materials, it can rapidly determine the diversity of metabolites, flavor substance content, types of flavor substances, microbial biomass, ethyl acetate content, acetic acid content, or ethanol content at the fermentation endpoint. Simultaneously, it allows for the screening of raw materials to regulate metabolite diversity. This invention enables the selection, selection, and combination of brewing raw materials such as sorghum, highland barley, rice, glutinous rice, barley, wheat, and corn. This invention can also be applied to other fermented food systems, such as wine, beer, vinegar, soy sauce, whiskey, and sauerkraut. Using this method to evaluate brewing raw materials is simple to operate, highly standardized, and objectively accurate.

Description

Technical Field

[0001] This invention relates to methods and applications for evaluating and screening food brewing raw materials, and belongs to the field of fermented food preparation technology. Background Technology

[0002] Fermented foods are a type of high-quality, uniquely flavored, and nutritious food made from grains, vegetables, and legumes through the metabolic processes of microbial communities. The raw materials for fermented foods are diverse and widely available. Various starchy and sugar-containing grains, such as sorghum, barley, rice, glutinous rice, barley, wheat, oats, rye, corn, indica rice, japonica rice, glutinous rice, millet, foxtail millet, yellow millet, buckwheat, soybeans, broad beans, peas, mung beans, red beans, and kidney beans, can all be used as fermentation raw materials. The quality of the fermentation raw materials has a significant impact on the nutritional value and flavor of fermented foods. Therefore, the selection of raw materials has a direct influence on the quality of fermented foods.

[0003] The selection of raw materials for conventional food brewing is mainly based on their physicochemical composition. For example, the selection of beer raw materials primarily includes the content of protein, starch, and polyphenols in malt. Furthermore, the selection of raw materials for traditional fermented foods, such as baijiu (Chinese liquor), huangjiu (yellow wine), and vinegar, focuses on the bulk density, moisture content, impurities, starch content, color, and aroma of the brewing raw materials. Currently, there is no selection method for traditional food brewing raw materials that is flavor-oriented. Given the diverse flavors of traditional fermented foods, there are no effective standards or simple selection methods for evaluating the impact of raw materials on food flavor and quality, or for finding suitable raw materials for food fermentation.

[0004] The impact of raw materials on fermentation includes providing nutrients, imparting flavor, and regulating the growth and flavor metabolism of microbial communities, ultimately affecting the flavor composition and quality of fermented foods. Among these, the function of raw materials in regulating the growth and flavor metabolism of microbial communities is the most important factor influencing the flavor and quality of fermented foods. However, currently, it is difficult to quantify and assess how raw materials regulate the growth and metabolism of microbial communities, resulting in an inability to evaluate the brewing function of raw materials, a lack of effective raw material evaluation methods, and an inability to effectively screen high-quality brewing raw materials. Summary of the Invention

[0005] [Technical Issues]

[0006] Brewing ingredients play an important role in the flavor of fermented foods, but there is currently no simple and effective method to screen and evaluate brewing ingredients. The technical problem to be solved by this invention is to provide a method for evaluating and screening food brewing ingredients.

[0007] [Technical Solution]

[0008] During fermentation, raw materials provide a variety of nutrients, including carbon and nitrogen sources, such as cereal proteins (albumin, globulin, prolamins, and gluten) and complex glycoside profiles formed by different sugars. Because different brewing raw materials have their own unique cereal protein and glycoside compositions, the diversity of flavor compounds varies among them during brewing. Determining the cereal protein and glycoside composition of raw materials is of positive guiding significance for the selection of brewing raw materials and the quality control of fermented foods, such as baijiu, wine, beer, vinegar, soy sauce, and sauerkraut.

[0009] To address the aforementioned problems, this invention provides a method and application for evaluating and screening food brewing raw materials.

[0010] The first objective of this invention is to provide a method for evaluating and screening brewing raw materials, comprising the following steps:

[0011] (1) Determine the content of cereal protein and sugar profile in food brewing raw materials;

[0012] (2) Substitute the content of cereal protein or sugar spectrum from step (1) into the prediction model and output the predicted value;

[0013] (3) Select brewing raw materials whose predicted values ​​are closest to the theoretical maximum value of the prediction model or the values ​​required for actual fermentation.

[0014] In one embodiment, the predicted values ​​include metabolite diversity values, flavor substance content, flavor substance types, microbial biomass, or ethanol content.

[0015] In one embodiment, the prediction model is constructed by using the content of cereal protein or glycosylation as the independent variable and the predicted value as the dependent variable, determining the independent variable most relevant to the dependent variable based on univariate analysis, and then using response surface methodology to fit the prediction model.

[0016] In one implementation, the prediction model is:

[0017]

[0018] The X i Let X be the i-th component of the n-dimensional variable X. j Let β0,β be the j-th component of the n-dimensional variable X. i ,β ij Let i,j = 1, 2, 3, ..., X be a constant. i For different types of proteins, X j For different types of sugar (i = 1, 2, 3, 4...);

[0019] The Y includes metabolite diversity, flavor substance content, flavor substance types, microbial biomass, ethyl acetate content, acetic acid content, or ethanol content.

[0020] In one embodiment, the cereal protein includes, but is not limited to, albumin, globulin, prolyl, and glutenin.

[0021] In one embodiment, the sugar spectrum includes, but is not limited to, maltose, glucose, fructose, rhamnose, galactose, arabinose, xylose, and cellobiose.

[0022] In one embodiment, the method for determining the grain protein content is as follows: the brewing raw materials are graded, extracted, and measured using the Osborne method.

[0023] In one embodiment, the method for determining the sugar content is as follows: the brewing raw materials are mixed with water, cooked at 80-110°C for 30-60 minutes, and then determined by chromatography.

[0024] In one implementation, when the predicted value is ethanol content (mg / g), the corresponding prediction model is:

[0025]

[0026] Where X1 is the glucose content (mg / g), X2 is the albumin content (mg / g), and X3 is the globulin content (mg / g).

[0027] In one implementation, when the predicted value is microbial biomass (Saccharomyces cerevisiae), the corresponding prediction model is:

[0028]

[0029] X1 represents glucose content (mg / g), X2 represents fructose content (mg / g), and X3 represents arabinose content (mg / g).

[0030] In one implementation, when the predicted value is microbial biomass (Pichia gondii), the corresponding prediction model is:

[0031]

[0032] Where X1 is the maltose content (mg / g), X2 is the fructose content (mg / g), and X3 is the xylose content (mg / g).

[0033] In one implementation, when the predicted value is the total content of flavor compounds, the corresponding prediction model is:

[0034]

[0035] Where X1 is the glucose content (mg / g), X2 is the fructose content (mg / g), and X3 is the arabinose content (mg / g).

[0036] In one implementation, when the predicted value is metabolite diversity, the corresponding prediction model is:

[0037] Y = -0.060 + 9.52 × 10 -3 ×X1+0.12×X2+0.059×X3-1.17×10 -3 ×X1X2+1.02×10 -3 ×X1X3-0.036×X2X3-6.49×10 5 ×X1 2 -6.21×10 -3 ×X2 2 -5.73×10 -3 ×X3 2

[0038] Where X1 is the glucose content (mg / g), X2 is the fructose content (mg / g), and X3 is the arabinose content (mg / g);

[0039] In one implementation, when the predicted value is ethyl acetate, an important flavor compound, the corresponding prediction model is:

[0040]

[0041] Where X1 is the glucose content (mg / g), X2 is the albumin content (mg / g), and X3 is the xylose content (mg / g).

[0042] In one implementation, when the predicted value is acetic acid, the corresponding prediction model is:

[0043]

[0044] Where X1 is the maltose content, X2 is the albumin content, and X3 is the globulin content.

[0045] In one embodiment, the brewing raw materials in step (1) include, but are not limited to, one or more of the following: sorghum, barley, rice, glutinous rice, barley, wheat, oats, rye, corn, indica rice, japonica rice, glutinous rice, millet, foxtail millet, yellow millet, buckwheat, soybean, broad bean, pea, mung bean, red bean and / or kidney bean.

[0046] The present invention also provides the application of the above method in food brewing.

[0047] This invention also provides an application for screening the optimal ratio of different brewing raw materials in the food brewing process.

[0048] This invention also provides its application in the selection and breeding of raw materials for food brewing.

[0049] Beneficial results:

[0050] 1. This invention provides a method for screening and evaluating food brewing raw materials. This invention discovers that the content of cereal protein and / or the content of glycosylation profiles in brewing raw materials can be used to screen and evaluate them, thereby achieving the purpose of regulating metabolite diversity, flavor compounds, and biomass. It can also rapidly predict the metabolite diversity, flavor compound content, and biomass of brewed products.

[0051] 2. This invention constructs a method for screening and evaluating food brewing raw materials by combining single-factor analysis with response surface methodology. It enables the selection, formulation, and combination of brewing raw materials such as sorghum, highland barley, rice, glutinous rice, barley, wheat, and corn. This invention can also be applied to other fermented food systems, such as wine, beer, vinegar, soy sauce, whiskey, and sauerkraut. Using this method to evaluate brewing raw materials is simple to operate, highly standardized, and objectively accurate. Attached Figure Description

[0052] Figure 1 Univariate regression linear correlation analysis of experimental and predicted values. Detailed Implementation

[0053] (1) The method for determining cereal protein is as follows:

[0054] 1) Fractional extraction was performed using the Osborne method, as follows: Utilizing the differences in solubility among different protein components, each component was extracted separately. A certain amount of grain raw material was taken, pulverized, and passed through a 40-mesh sieve. 100.0g of the sieved grain raw material was defatted with n-hexane, and ddH2O was added at a solid-liquid ratio of 1:8. The mixture was stirred continuously at room temperature for 2 hours, then centrifuged at 10000 r / min for 20 minutes at 4℃, and the supernatant was collected. The precipitate was subjected to the same operation, and the supernatants were combined and freeze-dried to obtain albumin. The precipitate was then extracted using 0.5mol / L NaCl, 70% ethanol, and 0.4% NaOH solutions, respectively, to obtain globulins, prolamins, and glutenins.

[0055] (2) The method for determining the sugar profile is as follows:

[0056] 1) Steaming: Soak sorghum and water in a ratio of 1:1.25 overnight at room temperature, then steam in a high-pressure steam sterilizer at 105°C for 45 minutes.

[0057] 2) Detection: Detection conditions: Thermo ICS 5000+ ion chromatograph; column: Dionex CarboPac PA10 (4×250mm); column oven temperature: 30℃; mobile phase: 18~200mmol / L NaOH solution; flow rate: 1.00mL / min; elution time: 45min; injection volume: 25μL; detection mode: Electrochemical Detection; working electrode: Gold (Au); reference electrode: Ag / AgCl.

[0058] (3) Simulated fermentation: Sorghum and water were soaked overnight at a ratio of 1:1.25 (mass fraction), and then steamed at 105℃ for 45 min. After steaming and cooling to room temperature, sorghum and Daqu (a type of starter culture) were mixed at a ratio of 9:1 (mass fraction), and the mixture (100g) was placed in a 100mL Erlenmeyer flask, sealed tightly, and allowed to ferment at 23.5℃ for 28 days. (Reference: Wang Zheng, Wang Shilei, Wu Qun, Xu Yan. Regulation of microbial community and metabolic diversity during Baijiu fermentation by cereal protein [J / OL]. Bulletin of Microbiology: 1-10 [2021-04-29].)

[0059] (4) Calculation of metabolite diversity: The contents of different metabolites at each fermentation time were normalized, and then the average value of the normalized metabolite contents at each fermentation time was taken as the metabolite diversity. (Reference: Wang Zheng, Wang Shilei, Wu Qun, Xu Yan. Regulation of microbial community and its metabolic diversity by cereal protein during Baijiu fermentation [J / OL]. Bulletin of Microbiology: 1-10 [2021-04-29].)

[0060] Example 1: Evaluation Method for Food Brewing Raw Materials

[0061] Taking the construction of a predictive model for metabolite diversity as an example, the specific steps are as follows:

[0062] (1) Construction of prediction model: The prediction model based on the content of cereal protein or sugar spectrum of brewing raw materials was established by response surface methodology. Based on the range of albumin, globulin, glutenin, prolamin, glucose, fructose, arabinose, maltose, cellobiose, galactose, rhamnose and xylose content in the nine kinds of sorghum, glucose, fructose and arabinose were determined as key independent variables through single factor analysis experiments. The Box-Behnken experimental design was used to establish a prediction model with glucose, fructose and arabinose content as independent variables and metabolite diversity as dependent variable.

[0063] The response surface methodology was designed and the model evaluated using Design Expert 8.0 software, as follows:

[0064] 1) Quantitatively determine the content of glucose, fructose, and arabinose in nine types of sorghum;

[0065] 2) Simulated fermentation was carried out on 9 kinds of sorghum, and the diversity of metabolites was calculated based on the content of metabolites;

[0066] 3) Response surface methodology experimental design.

[0067] Two-factor, three-level experimental design combined with a central composite design and a three-factor, three-level Box-Behnken design were used respectively, with the contents of glucose, fructose, and arabinose determined in step 1) as independent variables and the diversity of metabolites as the dependent variable to determine the optimal composition.

[0068] Table 1. Range of key sugar content in raw materials

[0069]

[0070] Table 2. Experimental Design Factors and Levels

[0071]

[0072] 4) Data Analysis and Model Evaluation

[0073] Using a Box-Behnken design, a univariate regression linear correlation analysis was performed on the experimental and predicted values. The results are as follows: Figure 1 As shown, there is a close consistency between the experimental data and the predicted data.

[0074] Analysis of variance (ANOVA) was performed on the diversity of metabolites using Design Expert 8.0 software. The coefficients of the polynomial simulation equations are shown in Table 3. In the polynomial model, P < 0.05 indicates a significant linear relationship between the response value and each factor, demonstrating the high reliability of the response surface model. The P > 0.05 for the lack-of-fit term indicates that it is insignificant, meaning the unknown factors have a minimal impact on the experimental results. Therefore, the experimental model adequately fits the experimental data, and the equation is feasible. Correlation coefficient R0 2 =0.91, indicating a high degree of consistency between the actual value of metabolite diversity and the predicted value of the response surface methodology, with a small error.

[0075] Table 3. Analysis of variance using a multinomial model based on the diversity of metabolites of important sugars.

[0076]

[0077] X1, X2, and X3 represent glucose, fructose, and arabinose, respectively.

[0078] A quadratic polynomial model equation was obtained relating metabolite diversity (Y) to glucose content (X1), fructose content (X2), and arabinose content (X3):

[0079] Y = -0.060 + 9.52 × 10 -3 ×X1+0.12×X2+0.059×X3-1.17×10 -3 ×X1X2+1.02×10 -3 ×X1X3-0.036×X2X3-6.49×10 -5 ×X1 2 -6.21×10 -3 ×X2 2 -5.73×10 -3 ×X3 2

[0080] (2) Methods for screening and evaluating raw materials for food brewing

[0081] 1) Determination: Quantitatively determine the content of glucose, fructose and arabinose in sorghum.

[0082] 2) Evaluation: The glucose content (X1), fructose content (X2) and arabinose content (X3) detected in step 1) are used as input values ​​and substituted into the prediction model constructed in step (1) to output the metabolite diversity value Y; the quality of brewing sorghum is judged based on the level of Y value.

[0083] (3) Application of screening and evaluation methods for food brewing raw materials

[0084] 500g of sorghum was soaked in 625mL of water at room temperature overnight, then steamed at 105℃ for 45 minutes. 5g of the steamed sorghum was taken for analysis of cereal protein and sugar content. The results showed that the glucose content was 23.14mg / g, the fructose content was 0.14mg / g, the arabinose content was 0.85mg / g, and the predicted metabolite diversity was 0.20.

[0085] Example 2

[0086] Taking baijiu fermentation as an example, the sugar profile and protein composition of sorghum raw materials are the independent variables, and ethanol production is the dependent variable. The model is constructed as follows:

[0087]

[0088] Where Y is the ethanol content (mg / g), X1 is the glucose content (mg / g), X2 is the albumin content (mg / g), and X3 is the globulin content (mg / g); when the contents of glucose, albumin, and globulin are 30.25 mg / g, 1.61 mg / g, and 2.21 mg / g, respectively, the ethanol content is the highest, at 29.16 mg / g.

[0089] Plant breeding technicians can use calculated data to selectively breed grains that are optimal for food brewing. Food brewing technicians can use calculated data to selectively choose suitable grains for food brewing or to formulate appropriate brewing ingredients.

[0090] Example 3

[0091] Taking baijiu fermentation as an example, the sugar profile and protein composition of sorghum raw materials are the independent variables, and the growth of brewing yeast is the dependent variable. The model is constructed as follows:

[0092]

[0093] Where Y is the biomass of Saccharomyces cerevisiae (lg(copies / g)), X1 is the glucose content (mg / g), X2 is the fructose content (mg / g), and X3 is the arabinose content (mg / g); when the contents of glucose, fructose, and arabinose are 29.31 mg / g, 1.83 mg / g, and 1.39 mg / g, respectively, the biomass of Saccharomyces cerevisiae is the highest, at 6.26 (lg(copies / g)).

[0094] Plant breeding technicians can use calculated data to selectively breed grains that are optimal for food brewing. Food brewing technicians can use calculated data to selectively choose suitable grains for food brewing or to formulate appropriate brewing ingredients.

[0095] Example 4

[0096] Taking baijiu fermentation as an example, the sugar profile and protein composition of sorghum raw materials are the independent variables, and the growth of Pichia pastoris is the dependent variable. The model is constructed as follows:

[0097]

[0098] Where Y is the biomass of Pichia gondii (lg(copies / g)), X1 is the maltose content (mg / g), X2 is the fructose content (mg / g), and X3 is the xylose content (mg / g); when the contents of maltose, fructose, and xylose are 21.03 mg / g, 4.27 mg / g, and 0.54 mg / g, respectively, the biomass of Pichia gondii is the highest, at 8.48 (lg(copies / g)).

[0099] The proportions of various brewing ingredients can be adjusted based on the calculated contents of maltose, fructose, and xylose to regulate biomass. Plant breeders can use the calculated data to selectively breed grains optimal for food brewing. Food brewing technicians can use the calculated data to selectively choose suitable grains for food brewing or to adjust the proportions of brewing ingredients.

[0100] Example 5

[0101] Taking baijiu fermentation as an example, the sugar profile and protein composition of sorghum raw materials are the independent variables, and the total content of flavor substances is the dependent variable. The model is constructed as follows:

[0102]

[0103] Where Y represents the total flavor compounds, X1 represents the glucose content (mg / g), X2 represents the fructose content (mg / g), and X3 represents the arabinose content (mg / g). Based on the constructed model, sorghum samples were used to detect the content of cereal protein and sugar profile. The glucose content was 24.13 mg / g, the fructose content was 1.21 mg / g, and the arabinose content was 0.19 mg / g. The predicted total flavor compound content was 7.64 mg / g.

[0104] Plant breeding technicians can use calculated data to selectively breed grains that are optimal for food brewing. Food brewing technicians can use calculated data to selectively choose suitable grains for food brewing or to formulate appropriate brewing ingredients.

[0105] Example 6

[0106] Taking baijiu fermentation as an example, the sugar profile and protein composition of sorghum raw materials are the independent variables, and ethyl acetate, an important flavor compound, is the dependent variable. The model is constructed as follows:

[0107]

[0108] Where Y represents the ethyl acetate content, X1 represents the glucose content (mg / g), X2 represents the albumin content (mg / g), and X3 represents the xylose content (mg / g). Based on the constructed ethyl acetate production model, the ethyl acetate content produced after sorghum fermentation was predicted. 500g of R3 sorghum was soaked overnight in 625mL of water at room temperature, then steamed at 105℃ for 45 minutes. 5g of sorghum was taken for grain protein content analysis. The glucose content was 20.08mg / g, the albumin content was 1.54mg / g, and the xylose content was 0.14mg / g. The predicted ethyl acetate content was 0.89mg / g.

[0109] Plant breeding technicians can use calculated data to selectively breed grains that are optimal for food brewing. Food brewing technicians can use calculated data to selectively choose suitable grains for food brewing or to formulate appropriate brewing ingredients.

[0110] Example 7

[0111] Taking vinegar fermentation as an example, the sugar profile and protein composition of the rice raw material are the independent variables, and acetic acid is the dependent variable. The model is constructed as follows:

[0112]

[0113] Where Y represents acetic acid content, X1 represents maltose content, X2 represents albumin content, and X3 represents globulin content. Based on the constructed model, the acetic acid content produced after rice fermentation was predicted. 500g of rice was soaked overnight in 625mL of water at room temperature, then steamed at 105℃ for 45 minutes. 5g of the steamed rice was taken for analysis of cereal protein and glycoprotein content. The maltose content was 14.38mg / g, the albumin content was 1.21mg / g, the globulin content was 0.24mg / g, and the predicted acetic acid content was 0.64mg / g.

[0114] Plant breeding technicians can use calculated data to selectively breed grains that are optimal for food brewing. Food brewing technicians can use calculated data to selectively choose suitable grains for food brewing or to formulate appropriate brewing ingredients.

[0115] As can be seen from the results of this embodiment, the method of the present invention can conveniently and effectively screen brewing raw materials, and has important guiding significance for the production and quality improvement of fermented foods.

[0116] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A method for evaluating and screening food brewing raw materials, characterized in that, The specific steps are as follows: (1) Determine the content of cereal protein and sugar profile in brewing raw materials; (2) Substitute the content of cereal protein and / or glycosylation from step (1) into the prediction model and output the predicted value; (3) Select brewing raw materials whose predicted values ​​are closest to the theoretical maximum value of the prediction model or the values ​​required for actual fermentation; The predicted values ​​include, but are not limited to, microbial biomass, ethyl acetate content, acetic acid content, or ethanol content; The method for constructing the prediction model is to use the content of cereal protein and / or sugar spectrum as independent variables and the predicted value as the dependent variable, determine the independent variable most related to the dependent variable based on univariate analysis, and fit the model to obtain the prediction model. The prediction model is as follows: The X i Let X be the i-th component of the n-dimensional variable X. j Let j be the j-th component of the n-dimensional variable X. , , Let X be a constant, i, j=1, 2, 3…. i For different types of cereal proteins and / or sugars, X j For different types of sugar; The cereal proteins include albumin, globulin, prolamins, and glutenin; the sugar profile includes maltose, glucose, fructose, rhamnose, galactose, arabinose, xylose, and cellobiose.

2. The method according to claim 1, characterized in that, When the predicted value is ethanol, the corresponding prediction model is: Where X1 is the glucose content in mg / g, X2 is the albumin content in mg / g, and X3 is the globulin content in mg / g.

3. The method according to claim 1, characterized in that, When the predicted value is the biomass of Saccharomyces cerevisiae, the corresponding prediction model is: X1 represents glucose content in mg / g, X2 represents fructose content in mg / g, and X3 represents arabinose content in mg / g. .

4. The method according to claim 1, characterized in that, When the predicted value is Pichia pastoris biomass, the corresponding prediction model is: Where X1 is the maltose content in mg / g, X2 is the fructose content in mg / g, and X3 is the xylose content in mg / g.

5. The method according to claim 1, characterized in that, When the predicted value is ethyl acetate, the corresponding prediction model is: Where X1 is the glucose content in mg / g, X2 is the albumin content in mg / g, and X3 is the xylose content in mg / g.

6. The method according to claim 1, characterized in that, When the predicted value is acetic acid, the corresponding prediction model is: Where X1 is the maltose content, X2 is the albumin content, and X3 is the globulin content.

7. The application of the method described in any one of claims 1 to 6 in the screening of the optimal ratio of different brewing raw materials and the selection and breeding of food brewing raw materials during the food brewing process.

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